What Is CDISC and What Are CDISC Data Standards?

What is CDISC?

The Clinical Data Interchange Standards Consortium (CDISC) is a global not-for-profit organization that develops universal standards for data collected during clinical research. Before CDISC began in 1997, the lack of standardization of data made submission to regulatory agencies and sharing information globally needlessly difficult and rife with delays from submission to approval. These complications impeded therapies from making it to market to serve the patient populations that needed them. Seeing the need for standardization, Rebecca Kush, PhD., founded CDISC to increase the accessibility, interoperability, and reusability of data from clinical research. With volunteers’ collective knowledge and experience within the pharmaceutical industry, CDISC creates and communicates standards that support the acquisition, exchange, submission, and archive of data for medical and biopharmaceutical product development.

The consortium collaborates with global agencies, such as the U.S. Food and Drug Administration (FDA), European Medicines Agency (EMA), Japan’s Pharmaceuticals and Medical Devices Agency (PMDA), and China’s National Medical Products Administration (NMPA), to develop guidelines and requirements that influence the standards for clinical and nonclinical data.

What are CDISC Data Standards?

The data standards created by CDISC can be organized into four key categories:

1. Foundational: CDISC refers to the foundational standards as the “core principles for defining data standards.” The foundational standards include all content, e.g., clinical and non-clinical data, models, domains, questionnaires, ratings, and scales (QRS).
2. Data Exchange: These standards are created to facilitate data sharing across different information systems, including those that have not implemented the foundational CDISC standards.
3. Therapeutic Areas: This is a series of user guides (TAUGS) for therapeutic areas that serve as specified extensions for the foundational standards.
4. Terminology: CDSIC terminology is a glossary created to provide standardized naming conventions for expressions or values within foundational and therapeutic area standards.

This resource will identify four standards within CDISC foundational standards and provide insight into their implementation.

CDASH Standards: How Data is Collected

Clinical Data Acquisition Standards Harmonization (CDASH) establishes uniformity in collecting raw data to support traceability and organization. This standard is driven by how the data will likely be collected and ensures that reviewers and regulators can effectively compare data across studies, sponsors, and time. For example, a clinical trial participant’s weight may be recorded every visit. One clinical study may have weight labeled as “weight,” while another may use an abbreviation like “WT.” CDASH requires the same label for the value across all studies, increasing efficiency when the data is submitted to regulatory agencies.

SDTM and SEND Standards: How Clinical and Nonclinical Data is Organized

The Study Data Tabulation Model (SDTM) is arguably the most well-recognized and widely implemented CDISC standard. It outlines a universal standard for how to structure and build content for data sets for individual clinical study data, while the Standard for Exchange of Nonclinical Data (SEND) is an implementation of SDTM that provides the same structure to nonclinical data. Both SDTM and SEND are required by the Food and Drug Administration (FDA) in the United States and the Pharmaceuticals and Medical Devices Agency (PMDA) in Japan requires SDTM.

Additionally, SDTM and SEND define each segment of data as a “domain,” which enables the people reviewing the data to find the information they need with limited to no study-specific understanding. These domains provide structure to all data, including highly specialized fields like pharmacokinetics (PK).

ADaM Standards: How Datasets are Built for Analysis

The Analysis Data Model (ADaM) establishes a standardized way to create consistent and well-defined datasets for statistical analysis from SDTM organized data. ADaM provides predictable and precise uniformity to dataset creation, ensuring the statistical programming processes of creating tables, lists, and figures (TLFs) can be completed efficiently and with clear traceability to SDTM. The domains established within SDTM are crucial to determining what data can be combined and used within datasets, for example the ADaM dataset ADCP/ADNCA which is used for noncompartmental analysis (NCA) in PK.

Similarly to SDTM, the FDA and PMDA both require ADaM for submissions.

Are you CDISC Ready?

Allucent is an industry leader in CDISC standards. As a CDISC gold member (2021, 2022), our CDISC experts at Allucent are actively involved in all forms of CDISC standards and implementation. Make your CDASH, SEND, SDTM, and ADaM datasets work with your CRO to ensure proper implementation of CDISC. Allucent can help:

• Generate datasets for legacy, planned, and current studies
• Generate complete CDISC datasets (all domains, SDTM, ADaM, etc.)
• Work with your CRO on CDISC implementation and reconciliation
• Provide advice for regulatory submissions

Contact Allucent to learn more about CDISC and how the A-Team can help get your datasets CDISC ready